On a typical day, as many as 25,000 aircraft flights occur within the United States, and several times that number occur throughout the world. Most of these flights are safe. A few might exhibit safety issues. Many aircraft are equipped with instrumentation that collects from a few dozen parameters to a few thousand parameters every second for the full duration of the flight. These types of data have long been used for crash investigations but can also be used for routine monitoring of flight operations. The subject invention relates to the latter activity. This provides an opportunity to analyze this data to identify portions of flights that exhibit safety issues. Aviation experts review these flights and recommend appropriate actions as a result.
Flight data, recorded during aircraft flight, consist of a series of parameter values. Each parameter describes a particular aspect of flight. Some parameters relate to continuous data such as altitude and airspeed. Other parameters assume a relatively small number of discrete values (e.g., two or three), such as thrust reverser position or flight guidance or autopilot command mode. Parameter measurements are usually made once per second although they may be recorded more or less frequently. Hundreds or even thousands of parameters may be collected for each second of an entire flight. These data are recorded for thousands of flights. The resulting data for an even modest size set of flights are voluminous.
Conventional methods of finding anomalous flights in bodies of digital flight data require users to pre-define the operational patterns that constitute unwanted performances. This can be a hit-or-miss process, requiring the experience and knowledge of experts in aviation operations, and it only identifies occurrences that specifically match the pre-defined condition. A conventional flight data analysis tool will find the patterns it is told to look for in flight data, but the tool is blind to newly emergent patterns for which the tool has not been programmed to look. The invention overcomes this deficiency because it does not require any pre-specification of what to look for in bodies of flight data.
Naturally most flights are typical and exhibit no safety issues. A very few flights stand out as atypical based values displayed by the data. These flights may be atypical due to one flight parameter being very unusual or multiple parameters being moderately unusual. It turns out that these unusual flights often exhibit safety issues and thus are of interest to identify and refer to aviation safety experts for review. Additionally, these atypical flights might display safety issues in a manner never envisioned by safety experts; hence impossible to find using pre-defined exceedences as done by the current state of the practice.
What is needed is an approach that allows identification of the most important flight parameters, capture and characterization of the dynamic values of these important parameters, and application of a consistent analysis to identify aircraft flights which exhibit atypical characteristics. This could mean that one or more of these parameters exhibits atypical values with respect to a collection of a set of flights that collectively define “typical”. This could also mean that individual parameters were marginally atypical, but collectively atypical. The analysis must be extendable to a larger or smaller number of “important” parameters and should not depend upon choice of a fixed number of such parameters. The analysis allows the identification of atypical flights without limiting the nature of the atypicalities to envisionable or pre-defined conditions.
In summary, the current state of the art is to monitored flight data for specified exceedences (excessive speed, g-forces, and other easily definable characteristics that differ from standard operating procedures). This invention goes beyond that by detecting unusual events, statistical patterns, and trends without requiring the pre-definition of what to look for and without limiting the investigation to a small number of parameters. It does this by applying multivariate statistical/mathematical methods.